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Paper Citation Record · LEDGER

Adapting Multi-modal Large Language Model to Concept Drift From Pre-training Onwards

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.13459.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2405.13459 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:24:35.748210Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-15T19:58:00.929414Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b5b749d0-2b72-4444-89f2-f512a45d7bdb · inbound

Walking the Tightrope: Disentangling Beneficial and Detrimental Drifts in Non-Stationary Custom-Tuning cites this paper.

Walking the Tightrope: Disentangling Beneficial and Detrimental Drifts in Non-Stationary Custom-Tuning Adapting Multi-modal Large Language Model to Concept Drift From Pre-training Onwards

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-15T20:24:35.748210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:24:35.748210Z digest=sha256:4b7e28ca03b255b6401f9c3bc677899740c87cb0174ea998cf8c11b8c79bcf23

Observation f449bc94-d5d4-4afc-a642-ec09a36c6228 · inbound

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems cites this paper.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Adapting Multi-modal Large Language Model to Concept Drift From Pre-training Onwards

Reference 127

Resolution
metadata mismatch
local_arxiv, observed 2026-08-15T19:58:00.933409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T19:58:00.690438Z digest=sha256:8f6cabab95bf2ec532b207a75164ef1a68cf32a37692cc6144f3360fc9edc890